國中生學校學習與家庭關係困擾之群體異質性分析:以IRT Mixture Model
No Thumbnail Available
Date
2012-09-??
Authors
王郁琮
溫福星
Yu-Chung Lawrence Wang
Fur-Hsing Wen
Journal Title
Journal ISSN
Volume Title
Publisher
國立臺灣師範大學教育心理學系
Department of Educational Psychology, NTNU
Department of Educational Psychology, NTNU
Abstract
本研究旨在利用試題反應理論混合模式( IRT Mixture Model) ，探討國中生家庭與學校生活適應因素結構，並藉由潛在異質性分析，進一步瞭解家庭關係與學校生活適應困擾之潛在次群體o研究樣本為中部某市立國民中學全校一至三年級各18 班共1703 位學生。結果發現，三類別二因子IRTMixture 測量恆定模式在一系列競爭模型中，展現出最佳模式適配。其中，二因子代表學校學習與家庭適應困擾，二類別反應高家庭困擾與低家庭困擾，二族群的學習困擾分數無顯著差異。比對試題區辨度發現， IRT Mixture 與IRT 估計維持一致。與潛在類別分析(LeA) 分類結果交叉分析發現， IRT Mixture 分類著重於家庭困擾程度。文末並針對本研究結果在輔導與諮商實務意涵以及應用IRTMixture 於實徵研究資料所面臨之挑戰，做出具體討論與建議o
The study utilized IRT Mixture Model to investigate latent heterogeneity of school learning and family relationships to differentiate latent classes and disturbance severity of middle school students. Four statistical models were examined, including IRT, LCA, IRT Mixture (parameters constrained) and IRT Mixture (parameters non-constrained). Results show that two-class two-factor IRT mixture model with constrained parameters provides the best fit of our data. Two factors are school learning and family relationships, and the two classes are high-risk and normal groups. Estimations of IRT Mixture Model are comparable to those of IRT and LCA. In conclusion, mixture models demonstrate more modeling flexibility compared to 血ose of traditional statistical models, but they require larger sample size, longer computer running hours, and more difficulties in reaching algorithm convergence.
The study utilized IRT Mixture Model to investigate latent heterogeneity of school learning and family relationships to differentiate latent classes and disturbance severity of middle school students. Four statistical models were examined, including IRT, LCA, IRT Mixture (parameters constrained) and IRT Mixture (parameters non-constrained). Results show that two-class two-factor IRT mixture model with constrained parameters provides the best fit of our data. Two factors are school learning and family relationships, and the two classes are high-risk and normal groups. Estimations of IRT Mixture Model are comparable to those of IRT and LCA. In conclusion, mixture models demonstrate more modeling flexibility compared to 血ose of traditional statistical models, but they require larger sample size, longer computer running hours, and more difficulties in reaching algorithm convergence.